Documentation

Overview

MetaLab is a research tool for aggregating across studies in the cognitive development literature. Currently, MetaLab contains722 effect sizes across meta-analyses in 14 domains of cognitive development, based on data from 204 papers collecting 10771 subjects. These studies can be used to obtain better estimates of effect sizes across different domains, methods, and ages. Using our power calculator, researchers can use these estimates to plan appropriate sample sizes for prospective studies. More generally, MetaLab can be used as a theoretical tool for exploring patterns in development across language acquisition domains.

Documentation

  • For more information about individual datasets, see the Datasets tab.
  • For information about our statistical approach throughout the site, see the Statistical Approach tab.
  • For information about the field structure of the site, see the Field Specification tab.
  • For information about doing meta-analyses and contributing to MetaLab, see the Contribute page.

Caveat

Please note that data and visualizations are under development at the moment (Winter 2017) and should not be taken as definitive.

Datasets

This page provides an overview of all datasets in MetaLab at this moment. All datasets have more detailed descriptions available through their home pages.

Cognitive development

Categorization bias

  • Citation:
  • N papers = 9, N effect sizes = 80
  • In a triad-task, bias to generalize to taxonomic as opposed to thematic alternative.
  • Curator: Molly Lewis

Early language

Function word segmentation

  • Citation: Bergmann, C., & Cristia, A. (2016). Development of infants’ segmentation of words from native speech: A meta-analytic approach. Developmental Science, 19, 901–917
  • N papers = 5, N effect sizes = 17
  • Recognition of familiarized function words from natural speech using behavioral methods applied to infants 0 - 20 months of age.
  • Curator: Christina Bergmann

Gaze following

  • Citation: Frank, M. C., Lewis, M., & MacDonald, K. (2016). A performance model for early wordlearning. In Proceedings of the 38th Annual Conference of the Cognitive Science Society.
  • N papers = 12, N effect sizes = 33
  • Gaze-following using standard multi-alternative forced-choice paradigms, in infants and children from 3 months to two years.
  • Curator: Molly Lewis

Infant directed speech preference

  • Citation: Dunst, C. J., Gorman, E., & Hamby, D. W. (2012). Preference for infant-directed speech in preverbal young children. Center for Early Literacy Learning, 5(1).
  • N papers = 23, N effect sizes = 75
  • Looking times as a function of whether infant-directed vs. adult-directed speech is presented as stimulation, in infants aged 0 - 12 months.
  • Curator: Alex Cristia

Label advantage in concept learning

  • Citation:
  • N papers = 16, N effect sizes = 100
  • Previous research suggests that labels support the formation of novel categories in young children. This meta-analysis includes studies exploring this question that use a paradigm similar to Balaban and Waxman (1997), resulting in a selection of experiments using behavioral methods on infants 3 to 18 months.
  • Curator: Molly Lewis

Online word recognition

  • Citation: Frank, M. C., Lewis, M., & MacDonald, K. (2016). A performance model for early wordlearning. In Proceedings of the 38th Annual Conference of the Cognitive Science Society.
  • N papers = 6, N effect sizes = 15
  • Online word recognition of familiar words using two-alternative forced choice preferential looking. Experiments in this MA follow Fernald et al. (1998) and were included based on their reporting of reaction time as well as effect size.
  • Curator: Molly Lewis

Phonotactic learning

  • Citation: Cristia, A. (in preparation). Infants’ phonology learning in the lab.
  • N papers = 15, N effect sizes = 47
  • Laboratory experiments in which infants are exposed to a set of pseudo-words that could support a phonotactic generalization, and thereafter tested on their preference between new types that follow or violate the familiarized phonotactics.
  • Curator: Alex Cristia

Pointing and vocabulary (longitudinal)

  • Citation: Colonnesi, C., Stamsa, G. J., Kostera, I., & Noomb, M. J. (2010). The relation between pointing and language development: A meta-analysis. Developmental Review, 30, 352–366.
  • N papers = 18, N effect sizes = 18
  • The pointing gesture has been argued to be related to the development of language. This meta-analysis explores the relationship between pointing and language development, both concurrently and longitudinally.
  • Curator: Molly Lewis

Pointing and vocabulary (concurrent)

  • Citation: Colonnesi, C., Stamsa, G. J., Kostera, I., & Noomb, M. J. (2010). The relation between pointing and language development: A meta-analysis. Developmental Review, 30, 352–366.
  • N papers = 12, N effect sizes = 12
  • The pointing gesture has been argued to be related to the development of language. This meta-analysis explores the relationship between pointing and language development, both concurrently and longitudinally.
  • Curator: Molly Lewis

Statistical sound category learning

  • Citation: Cristia, A. (in preparation). Infants’ phonology learning in the lab.
  • N papers = 10, N effect sizes = 18
  • Laboratory experiments in which infants are exposed to a set of syllables that have been generated using a bimodal distribution of acoustic cues or not, and infants are tested on their discrimination of two sounds differing along the same dimension.
  • Curator: Alex Cristia

Statistical word segmentation

  • Citation: Black, A. & Bergmann, C. (2017). Quantifying infants’ statistical word segmentation: A meta-analysis. In: Proceedings of the 39th Annual Conference of the Cognitive Science Society, pp. 124-129, Austin, TX: Cognitive Science Society
  • N papers = 21, N effect sizes = 73
  • Can infants segment words from artificial mini-languages based on syllable-level statistics?
  • Curator: Christina Bergmann

Sound symbolism

  • Citation: Lammertink, I., Fort, M., Peperkamp, S., Fikkert, P., Guevara-Rukoz, A., & Tsuji, S. (2016). SymBuki: A meta-analysis on the sound-symbolic bouba-kiki effect in infants andtoddlers. Poster presented at the XXI Biennial International Congress of Infant Studies, New Orleans, USA.
  • N papers = 11, N effect sizes = 44
  • Do infants recognize systematic relationships between form and meaning, e.g. that bouba tends to refer to a round object and kiki tends to refer to a spiky one?
  • Curator: Sho Tsuji

Vowel discrimination (native)

  • Citation: Tsuji, S. & Cristia, A. (2014). Perceptual attunement in vowels: A meta-analysis. Developmental Psychobiology, 56(2), 179-191.
  • N papers = 32, N effect sizes = 143
  • Discrimination of native-language vowels, including results from behavioral, electrophysiological, and neuroimaging methods applied to infants 0 - 14 months of age.
  • Curator: Sho Tsuji

Vowel discrimination (non-native)

  • Citation: Tsuji, S. & Cristia, A. (2014). Perceptual attunement in vowels: A meta-analysis. Developmental Psychobiology, 56(2), 179-191.
  • N papers = 14, N effect sizes = 47
  • Discrimination of non-native language vowels, including results from behavioral, electrophysiological, and neuroimaging methods applied to infants 0 - 14 months of age.
  • Curator: Sho Tsuji

How to contribute?

We welcome researchers interested in contributing to Metalab. Please contact us at metalab-project@googlegroups.com

Contributions can take various forms:

  • Adding unpublished data. Ideally we can link to a report on the data, for example on OSF. Then contact the curator with a link and if possible all necessary data.
  • Suggesting and/or adding published papers not yet included. Simply email the curator, ideally with a pdf. If you are the author, try to make sure that all necessary data is either included or provided in your email.
  • Creating new meta-analyses. If you are interested in creating a new meta-analysis, check out the tutorial page.
  • Contribute existing meta-analyses. If you already have a completed meta-analysis, make sure to use the MetaLab template and get in touch about integrating it into MetaLab. Check out the tutorial page on how to edit your meta-analysis in the MetaLab format.

Statistical Approach

Overview

All analyses on the site are conducted with the metafor package (Viechtbauer, 2010).

Effect Size Computation

Effect size computation is handled by a script, compute_es.R.

Several pre-existing MAs deal with special cases, and these are listed in the script.

Except where noted, formulas are from Hedges & Olkin’s textbook.

Statistical Models

The visualizations page uses a multi-level random effects meta-analysis ( rma.mv function of metafor). Random-effect models assume that the true effect can vary between different studies, and therefore allow random effects for each data point. The model also specifies random effects on the level of each paper, since studies within a paper can be assumed to be more similar to each other than to studies from different papers. In addition, we allow correlated random effects within each paper to account for cases where the same infants contributed data to multiple rows.

The meta-analytic models are accessible in the script server.R

Field Specifications

This page gives the full specification for each field in the metalab dataset, including: required fields (which must be included for every MA), optional fields (which are only used for some MAs), and derived fields (which are computed by the site).

Required fields

Optional fields

Derived fields